Update api.py

api.py

@@ -146,27 +146,6 @@ _ = model(dummy_input)  # 모델이 빌드됨
 model.load_weights("Flexi.weights.h5")      
 print("모델 가중치 로드 완료!")      
 
-  
-def is_greedy_response_acceptable(text):  
-    text = text.strip()  
-  
-    # 너무 짧은 문장 거르기  
-    if len(text) < 5:  
-        return False  
-  
-    # 단어 수 너무 적은 것도 거름  
-    if len(text.split()) < 3:  
-        return False  
-  
-    # ㅋㅋㅋ 같은 자모 연속만 있으면 거름 (단, 'ㅋㅋ' 포함되면 허용)  
-    if re.search(r'[ㄱ-ㅎㅏ-ㅣ]{3,}', text) and 'ㅋㅋ' not in text:  
-        return False  
-  
-    # 문장 끝이 어색한 경우 (다/요/죠 등 일반적 형태로 끝나지 않으면 거름)  
-    if not re.search(r'(다|요|죠|다\.|요\.|죠\.|다!|요!|죠!|\!|\?|\.)$', text):  
-        return False  
-  
-    return True
 
 def generate_text_sample(model, prompt, max_len=100, max_gen=98,
                          temperature=0.8, top_k=55, top_p=0.95, min_len=12):
@@ -197,10 +176,9 @@ def generate_text_sample(model, prompt, max_len=100, max_gen=98,
             sorted_indices = np.argsort(probs)[::-1]
             sorted_probs = probs[sorted_indices]
             cumulative_probs = np.cumsum(sorted_probs)
-            # 누적 확률이 top_p 초과하는 토큰들은 제거
             cutoff_index = np.searchsorted(cumulative_probs, top_p, side='right')
             probs_to_keep = sorted_indices[:cutoff_index+1]
-            
+
             mask = np.ones_like(probs, dtype=bool)
             mask[probs_to_keep] = False
             probs[mask] = 0
@@ -217,16 +195,14 @@ def generate_text_sample(model, prompt, max_len=100, max_gen=98,
         decoded = decoded.strip()
 
         if len(generated) >= min_len and (next_token == end_id or decoded.endswith(('요', '다', '.', '!', '?'))):
-            if is_greedy_response_acceptable(decoded):
-                return decoded
-            else:
-                continue
+            return decoded
 
     decoded = sp.decode(generated)
     for t in ["<start>", "<sep>", "<end>"]:
         decoded = decoded.replace(t, "")
     return decoded.strip()
 
+
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.decomposition import TruncatedSVD
 from sklearn.metrics.pairwise import cosine_similarity